Flowchart
library(GENIE3)
library(doParallel)
library(igraph)
library(tidyverse)
library(DT)
library(reticulate)
library(learn2count)
library(rbenchmark)
library(reshape2)
library(gridExtra)
library(DiagrammeR)
library(pROC)
library(JRF)
library(DiagrammeRsvg)
library(rsvg)
library(RColorBrewer)
library(rbenchmark)
library(ZILGM)
library(patchwork)
use_python("/usr/bin/python3", required = TRUE)
arboreto <- import("arboreto.algo")
pandas <- import("pandas")
numpy <- import("numpy")
execution_times <- list()
source("generate_adjacency.R")
source("symmetrize.R")
source("pscores.R")
source("plotg.R")
source("compare_consensus.R")
source("create_consensus.R")
source("earlyj.R")
source("plotROC.R")
source("cutoff_adjacency.R")
source("infer_networks.R")
grViz_output <- DiagrammeR::grViz("
digraph biological_workflow {
# Set up the graph attributes
graph [layout = dot, rankdir = TB]
# Define consistent node styles
node [shape = rectangle, style = filled, color = lightblue, fontsize = 12]
# Define nodes for each step
StartNode [label = 'Ground Thruth - String Regulatory Network', shape = oval, color = seagreen, fontcolor = black]
AdjacencyMatrix [label = 'Thruth Adjacency Matrix', shape = rectangle, color = seagreen]
SimulateData [label = 'Simulate Single-Cell Data', shape = rectangle, color = goldenrod]
# Reconstruction using Three Packages
LateIntegration [label = 'Late\nIntegration', shape = oval, color = khaki]
EarlyIntegration [label = 'Early\nIntegration', shape = oval, color = khaki]
Jointanalysis [label = 'Joint\nanalysis', shape = oval, color = khaki]
# Process
earlyj [label = 'earlyj.R', shape=diamond, color=lightblue, fontcolor=black]
networkinference [label = 'infer_networks.R\nGENIE3\nGRNBoost2\nZILGM\nJRF', shape = rectangle, color = goldenrod, fontcolor=black]
symmetrize [label = 'symmetrize.R', shape = rectangle, color = goldenrod, fontcolor=black]
plotROC [label = 'plotROC.R', shape=diamond, color=lightblue, fontcolor=black]
generateadjacency [label='generate_adjacency.R\nWeighted Adjacency', shape=rectangle, color=goldenrod, fontcolor=black]
cutoffadjacency [label='cutoff_adjacency.R\nBinary Adjacency', shape=rectangle, color=goldenrod, fontcolor=black]
pscores [label='pscores.R\nTPR\nFPR\nF1\nAccuracy\nPrecision', shape=diamond, color=lightblue, fontcolor=black]
voting [label='Voting\nUnion\nIntersection', shape=diamond, color=lightblue, fontcolor=black]
plotgcompare [label='plotg.R\ncompare_consesus.R\nPlot Graphs', shape=rectangle, color=goldenrod, fontcolor=black]
# Define the workflow structure
StartNode -> AdjacencyMatrix
AdjacencyMatrix -> SimulateData
SimulateData -> LateIntegration
SimulateData -> EarlyIntegration
SimulateData -> Jointanalysis
EarlyIntegration -> earlyj
earlyj -> networkinference
LateIntegration -> networkinference
Jointanalysis -> networkinference
networkinference -> symmetrize
symmetrize -> plotROC
symmetrize -> generateadjacency
generateadjacency -> cutoffadjacency
cutoffadjacency -> pscores
cutoffadjacency -> voting
voting -> plotgcompare
voting -> pscores
}
")
svg_code <- export_svg(grViz_output)
rsvg::rsvg_png(charToRaw(svg_code), "./../analysis/flowchart.png")
grViz_output
Tcell Ground Truth
adjm <- read.table("./../data/adjacency_matrix.csv", header = T, row.names = 1, sep = ",") %>% as.matrix()
diag(adjm) <- 0
adjm %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "Ground Truth")
gtruth <- igraph::graph_from_adjacency_matrix(adjm, mode = "undirected", diag = F)
num_nodes <- vcount(gtruth)
num_edges <- ecount(gtruth)
set.seed(1234)
plot(gtruth,
main = paste("Ground Truth\nNodes:", num_nodes, "Edges:", num_edges),
vertex.label.color = "black",
vertex.size = 6,
edge.width = 2,
vertex.label = NA,
vertex.color = "steelblue",
layout = igraph::layout_with_fr)
Simulate Data
ncell <- 500
nodes <- nrow(adjm)
set.seed(1130)
mu_values <- c(3, 6, 9)
count_matrices <- lapply(1:3, function(i) {
set.seed(1130 + i)
mu_i <- mu_values[i]
count_matrix_i <- simdata(n = ncell, p = nodes, B = adjm, family = "ZINB",
mu = mu_i, mu_noise = 1, theta = 0.5, pi = 0.2)
count_matrix_df <- as.data.frame(count_matrix_i)
colnames(count_matrix_df) <- colnames(adjm)
rownames(count_matrix_df) <- paste("cell", 1:nrow(count_matrix_df), sep = "")
return(count_matrix_df)
})
count_matrices[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "Simulated count matrix")
saveRDS(count_matrices, "./../analysis/count_matrices.RDS")
Matrices Integration
Late Integration
GENIE3
set.seed(1234)
tictoc::tic("GENIE3 late")
genie3_late <- infer_networks(count_matrices, method="GENIE3")
saveRDS(genie3_late, "./../analysis/genie3_late.RDS")
execution_times[['GENIE3 late']] <- tictoc::toc(log = TRUE)$toc[[1]]
## GENIE3 late: 132.907 sec elapsed
genie3_late[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 output")
symmetrize Output and ROC
source("plotROC.R")
genie3_late_wadj <- generate_adjacency(genie3_late, ground.truth = adjm)
sgenie3_late_wadj <- symmetrize(genie3_late_wadj, weight_function = "mean")
plotROC(sgenie3_late_wadj, adjm, plot_title = "ROC curve - GENIE3 Late Integration")
sgenie3_late_wadj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 symmetrize output")
Generate Adjacency and Apply Cutoff
sgenie3_late_adj <- cutoff_adjacency(count_matrices = count_matrices,
weighted_adjm_list = sgenie3_late_wadj,
ground.truth = adjm,
n = 2,
method = "GENIE3")
## Matrix 1 Mean 95th Percentile Cutoff: 0.009957001
## Matrix 2 Mean 95th Percentile Cutoff: 0.00990153
## Matrix 3 Mean 95th Percentile Cutoff: 0.00968373
sgenie3_late_adj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 adjacency")
Comparison with the Ground Truth
scores.genie3.late.all <- pscores(adjm, sgenie3_late_adj)
scores.genie3.late.all$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
plots <- plotg(sgenie3_late_adj)
consesusm <- create_consensus(sgenie3_late_adj, method="vote")
consesusu <- create_consensus(sgenie3_late_adj, method="union")
scores.genie3.late <- pscores(adjm, list(consesusm))
scoresu.genie3.late <- pscores(adjm, list(consesusu))
scores.genie3.late$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
scores.genie3.late$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
ajm_compared <- compare_consensus(consesusm, adjm)
ajm_compared <- compare_consensus(consesusu, adjm)
GRNBoost2
set.seed(1234)
tictoc::tic("GRNBoost2 late")
grnb_late <- infer_networks(count_matrices, method="GRNBoost2")
saveRDS(grnb_late, "./../analysis/grnb_late.RDS")
execution_times[['GRNBoost2 late']] <- tictoc::toc(log = TRUE)$toc[[1]]
## GRNBoost2 late: 9.241 sec elapsed
grnb_late[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 output")
symmetrize Output and ROC
grnb_late_wadj <- generate_adjacency(grnb_late, ground.truth = adjm)
sgrnb_late_wadj <- symmetrize(grnb_late_wadj, weight_function = "mean")
plotROC(sgrnb_late_wadj, adjm, plot_title = "ROC curve - GRNBoost2 Late Integration")
sgrnb_late_wadj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 symmetrize output")
Generate Adjacency and Apply Cutoff
sgrnb_late_adj <- cutoff_adjacency(count_matrices = count_matrices,
weighted_adjm_list = sgrnb_late_wadj,
ground.truth = adjm,
n = 2,
method = "GRNBoost2")
## Matrix 1 Mean 95th Percentile Cutoff: 0.8679026
## Matrix 2 Mean 95th Percentile Cutoff: 0.8586811
## Matrix 3 Mean 95th Percentile Cutoff: 0.8864974
sgrnb_late_adj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 adjacency")
Comparison with the Ground Truth
scores.grn.late.all <- pscores(adjm, sgrnb_late_adj)
scores.grn.late.all$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
plots <- plotg(sgrnb_late_adj)
consesusm <- create_consensus(sgrnb_late_adj, method="vote")
consesusu <- create_consensus(sgrnb_late_adj, method="union")
scores.grn.late <- pscores(adjm, list(consesusm))
scoresu.grn.late <- pscores(adjm, list(consesusu))
scores.grn.late$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
scoresu.grn.late$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
ajm_compared <- compare_consensus(consesusm, adjm)
ajm_compared <- compare_consensus(consesusu, adjm)
ZILGM Park
set.seed(1234)
tictoc::tic("ZILGM late")
zilgm_late <- infer_networks(count_matrices_list = count_matrices, method = "ZILGM", adjm = adjm)
## learning for NBII graphical model
## nlambda : 50
## penalty function : LASSO
## update type : IRLS
## Conducting sampling in progress : 10 %
Conducting sampling in progress : 20 %
Conducting sampling in progress : 30 %
Conducting sampling in progress : 40 %
Conducting sampling in progress : 50 %
Conducting sampling in progress : 60 %
Conducting sampling in progress : 70 %
Conducting sampling in progress : 80 %
Conducting sampling in progress : 90 %
Conducting sampling in progress : 100 %
learning for NBII graphical model
## nlambda : 50
## penalty function : LASSO
## update type : IRLS
## Conducting sampling in progress : 10 %
Conducting sampling in progress : 20 %
Conducting sampling in progress : 30 %
Conducting sampling in progress : 40 %
Conducting sampling in progress : 50 %
Conducting sampling in progress : 60 %
Conducting sampling in progress : 70 %
Conducting sampling in progress : 80 %
Conducting sampling in progress : 90 %
Conducting sampling in progress : 100 %
learning for NBII graphical model
## nlambda : 50
## penalty function : LASSO
## update type : IRLS
## Conducting sampling in progress : 10 %
Conducting sampling in progress : 20 %
Conducting sampling in progress : 30 %
Conducting sampling in progress : 40 %
Conducting sampling in progress : 50 %
Conducting sampling in progress : 60 %
Conducting sampling in progress : 70 %
Conducting sampling in progress : 80 %
Conducting sampling in progress : 90 %
Conducting sampling in progress : 100 %
Assigned lamb to lambda_results[[ 1 ]] with 50 matrices.
## Assigned lamb to lambda_results[[ 2 ]] with 50 matrices.
## Assigned lamb to lambda_results[[ 3 ]] with 50 matrices.
saveRDS(zilgm_late, "./../analysis/zilgm_late.RDS")
execution_times[['ZILGM late']] <- tictoc::toc(log = TRUE)$toc[[1]]
## ZILGM late: 3978.19 sec elapsed
est_graphs <- zilgm_late$network_results
lambdas <- zilgm_late$lambda_results
scores.zilgm1 <- pscores(adjm, lambdas[[1]])
scores.zilgm2 <- pscores(adjm, lambdas[[2]])
scores.zilgm3 <- pscores(adjm, lambdas[[3]])
m1 <- scores.zilgm1$Statistics %>% mutate(K="M1")
m2 <- scores.zilgm2$Statistics %>% mutate(K="M2")
m3 <- scores.zilgm3$Statistics %>% mutate(K="M3")
allm <- rbind(m1,m2,m3)
ggplot(allm, aes(x=FPR, y=TPR, color=K)) +
geom_point() +
geom_line(size=1.2) +
labs(x="False Positive Rate (1 - Specificity)",
y="True Positive Rate (Sensitivity)") +
theme_minimal() +
theme(legend.position = "bottom")
consensus_matrices <- vector("list", 50)
for (i in 1:50) {
ranklambda <- list(lambdas[[1]][[i]], lambdas[[2]][[i]], lambdas[[3]][[i]])
consensus_matrices[[i]] <- create_consensus(ranklambda, method="vote")
}
scores.zilgm.voting <- pscores(adjm, consensus_matrices)
scores.zilgm.voting$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
ggplot(scores.zilgm.voting$Statistics, aes(x=FPR, y=TPR)) +
geom_point() +
geom_line(size=1.2) +
labs(x="False Positive Rate (1 - Specificity)",
y="True Positive Rate (Sensitivity)") +
theme_minimal() +
theme(legend.position = "bottom")
Comparison with the Ground Truth
scores.zilgm.late.all <- pscores(adjm, est_graphs)
scoresu.zilgm.late.all <- pscores(adjm, est_graphs)
scores.zilgm.late.all$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
scoresu.zilgm.late.all$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
plots <- plotg(est_graphs)
consesusm <- create_consensus(est_graphs, method="vote")
consesusu <- create_consensus(est_graphs, method="union")
scores.zilgm.late <- pscores(adjm, list(consesusm))
scoresu.zilgm.late <- pscores(adjm, list(consesusu))
scores.zilgm.late$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
scoresu.zilgm.late$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
ajm_compared <- compare_consensus(consesusm, adjm)
ajm_compared <- compare_consensus(consesusu, adjm)
Early Integration
early_matrix <- list(earlyj(count_matrices))
GENIE3
set.seed(1234)
tictoc::tic("GENIE3 early")
genie3_early <- infer_networks(early_matrix, method="GENIE3")
execution_times[['GENIE3 early']] <- tictoc::toc(log = TRUE)$toc[[1]]
## GENIE3 early: 152.035 sec elapsed
saveRDS(genie3_early, "./../analysis/genie3_early.RDS")
genie3_early[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 output")
symmetrize Output and ROC
genie3_early_wadj <- generate_adjacency(genie3_early, ground.truth = adjm)
sgenie3_early_wadj <- symmetrize(genie3_early_wadj, weight_function = "mean")
plotROC(sgenie3_early_wadj, adjm, plot_title = "ROC curve - GENIE3 Early Integration")
sgenie3_early_wadj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 symmetrize output")
Generate Adjacency and Apply Cutoff
sgenie3_early_adj <- cutoff_adjacency(count_matrices = early_matrix,
weighted_adjm_list = sgenie3_early_wadj,
ground.truth = adjm,
n = 2,
method = "GENIE3")
## Matrix 1 Mean 95th Percentile Cutoff: 0.01013741
sgenie3_early_adj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GENIE3 adjacency")
Comparison with the Ground Truth
scores.genie3.early <- pscores(adjm, sgenie3_early_adj)
scores.genie3.early$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
plots <- plotg(sgenie3_early_adj)
ajm_compared <- compare_consensus(sgenie3_early_adj[[1]], adjm)
GRNBoost2
set.seed(1234)
tictoc::tic("GRNBoost2 early")
grnb_early <- infer_networks(early_matrix, method="GRNBoost2")
execution_times[['GRNBoost2 early']] <- tictoc::toc(log = TRUE)$toc[[1]]
## GRNBoost2 early: 11.234 sec elapsed
saveRDS(grnb_early, "./../analysis/grnb_early.RDS")
grnb_early[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 output")
symmetrize Output and ROC
grnb_early_wadj <- generate_adjacency(grnb_early, ground.truth = adjm)
sgrnb_early_wadj <- symmetrize(grnb_early_wadj, weight_function = "mean")
plotROC(sgrnb_early_wadj, adjm, plot_title = "ROC curve - GRNBoost2 Early Integration")
grnb_early_wadj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 symmetrize output")
Generate Adjacency and Apply Cutoff
sgrnb_early_adj <- cutoff_adjacency(count_matrices = early_matrix,
weighted_adjm_list = sgrnb_early_wadj,
ground.truth = adjm,
n = 2,
method = "GRNBoost2")
## Matrix 1 Mean 95th Percentile Cutoff: 4.281737
sgrnb_early_adj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "GRNBoost2 adjacency")
Comparison with the Ground Truth
scores.grn.early <- pscores(adjm, sgrnb_early_adj)
scores.grn.early$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
plots <- plotg(sgrnb_early_adj)
ajm_compared <- compare_consensus(sgrnb_early_adj[[1]], adjm)
ZILGM Park
set.seed(1234)
tictoc::tic("ZILGM early")
zilgm_late <- infer_networks(count_matrices_list = early_matrix, method = "ZILGM", adjm = adjm)
## learning for NBII graphical model
## nlambda : 50
## penalty function : LASSO
## update type : IRLS
## Conducting sampling in progress : 10 %
Conducting sampling in progress : 20 %
Conducting sampling in progress : 30 %
Conducting sampling in progress : 40 %
Conducting sampling in progress : 50 %
Conducting sampling in progress : 60 %
Conducting sampling in progress : 70 %
Conducting sampling in progress : 80 %
Conducting sampling in progress : 90 %
Conducting sampling in progress : 100 %
Assigned lamb to lambda_results[[ 1 ]] with 50 matrices.
saveRDS(zilgm_late, "./../analysis/zilgm_early.RDS")
execution_times[['ZILGM early']] <- tictoc::toc(log = TRUE)$toc[[1]]
## ZILGM early: 3605.811 sec elapsed
est_graphs <- zilgm_late$network_results
lambdas <- zilgm_late$lambda_results
scores.zilgm1 <- pscores(adjm, lambdas[[1]])
m1 <- scores.zilgm1$Statistics %>% mutate(K="M1")
ggplot(m1, aes(x=FPR, y=TPR, color=K)) +
geom_point() +
geom_line(size=1.2) +
labs(x="False Positive Rate (1 - Specificity)",
y="True Positive Rate (Sensitivity)") +
theme_minimal() +
theme(legend.position = "bottom")
Comparison with the Ground Truth
scores.zilgm.early <- pscores(adjm, est_graphs)
scores.zilgm.early$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
ajm_compared <- compare_consensus(est_graphs[[1]], adjm)
Joint Integration
Joint Random Forest
#https://cran.r-project.org/src/contrib/Archive/JRF/
#install.packages("/home/francescoc/Downloads/JRF_0.1-4.tar.gz", repos = NULL, type = "source")
#jrf_mat <- infer_networks(count_matrices, method="JRF")
jrf_matrices <- lapply(count_matrices, t)
jrf_matrices_norm <- lapply(jrf_matrices,function(x) {
(x - mean(x)) / sd(x)
})
genes <- rownames(jrf_matrices_norm[[1]])
set.seed(1234)
tictoc::tic("JRF_out")
netout <- JRF(X = jrf_matrices_norm,
genes.name = genes,
ntree = 500,
mtry = round(sqrt(length(genes) - 1)))
execution_times[['JRF_out']] <- tictoc::toc(log = TRUE)$toc[[1]]
## JRF_out: 1213.373 sec elapsed
netout %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "JRF output")
out.perm <- Run_permutation(jrf_matrices_norm,mtry=round(sqrt(length(genes)-1)),ntree=500, genes,3)
#out <- JRF_permutation(jrf_matrices_norm,mtry=round(sqrt(length(genes)-1)),ntree=500,genes,2)
#final.net <- JRF_network(netout,out.perm,0.001)
#final.net
Finalnet
myJRF_network <- function(out.jrf, out.perm, TH) {
nclasses <- dim(out.perm)[3]
M <- dim(out.perm)[2]
out <- vector("list", nclasses)
for (net in 1:nclasses) {
j.np <- sort(out.jrf[, 2 + net], decreasing = TRUE)
FDR <- matrix(0, dim(out.perm)[1], 1)
th <- NULL
for (s in 1:length(j.np)) {
FP <- sum(sum(out.perm[, , net] >= j.np[s])) / M
FDR[s] <- FP / s
if (FDR[s] > TH) {
th <- j.np[s]
break
}
}
out[[net]] <- out.jrf[out.jrf[, 2 + net] > th, seq(1, 2)]
}
return(out)
}
mynet <- myJRF_network(netout,out.perm,0.05)
mynet
## [[1]]
## gene1 gene2
## 341 EEF1B2 AHNAK
## 714 RPS5 APEX1
## 2992 FCGR3A CD14
## 3225 CD3D CD2
## 4106 CXCR4 CD3G
## 4179 PTPRC CD3G
## 4609 GZMK CD6
## 7756 KLRB1 GNLY
## 8438 RACK1 IGF1R
## 10659 PTPRC PRF1
## 10796 TFRC PTPRC
##
## [[2]]
## gene1 gene2
## 130 SPTBN1 ACTN1
## 797 FGR ARHGAP25
## 1043 CASP8 BCL2
## 1409 LTB BIRC3
## 1702 PDE3B CAMK4
## 2699 CCR8 CCR7
## 3225 CD3D CD2
## 3229 CD5 CD2
## 3267 GZMK CD2
## 3277 IL7R CD2
## 3852 CD3G CD3D
## 3858 CD8A CD3D
## 3892 GZMK CD3D
## 3909 KLRD1 CD3D
## 3911 LDLRAP1 CD3D
## 4012 GZMB CD3E
## 4179 PTPRC CD3G
## 4185 RPS5 CD3G
## 4202 TFRC CD3G
## 4507 ITGB2 CD52
## 4838 GZMB CD8A
## 4856 KLRB1 CD8A
## 4884 PTPRC CD8A
## 5444 PTPRC CXCR4
## 5881 RPS8 EIF3E
## 6903 TIGIT FCRL3
## 7291 FOXP3 FOXP1
## 7459 TNFRSF18 FOXP3
## 8008 KLRB1 GZMB
## 8171 KLRB1 GZMK
## 8257 LILRB1 HAVCR2
## 8438 RACK1 IGF1R
## 8501 NKG7 IL10RA
## 8835 TIGIT IL2RB
## 10230 SPTBN1 NCAM1
## 10712 SLC9A3R1 PRKCA
## 10785 SELL PTPRC
## 11411 TNFSF8 TNFRSF8
##
## [[3]]
## [1] gene1 gene2
## <0 rows> (or 0-length row.names)
#https://cran.r-project.org/src/contrib/Archive/JRF/
#install.packages("/home/francescoc/Downloads/JRF_0.1-4.tar.gz", repos = NULL, type = "source")
set.seed(1234)
tictoc::tic("JRF")
jrf_mat <- infer_networks(count_matrices, method="JRF")
execution_times[['JRF']] <- tictoc::toc(log = TRUE)$toc[[1]]
## JRF: 1203.537 sec elapsed
jrf_mat[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "JRF output")
Prepare the output
jrf_list <- list()
importance_columns <- grep("importance", names(jrf_mat[[1]]), value = TRUE)
for (i in seq_along(importance_columns)) {
# Select the 'gene1', 'gene2', and the current 'importance' column
df <- jrf_mat[[1]][, c("gene1", "gene2", importance_columns[i])]
# Rename the importance column to its original name (e.g., importance1, importance2, etc.)
names(df)[3] <- importance_columns[i]
# Add the data frame to the output list
jrf_list[[i]] <- df
}
saveRDS(jrf_list, "./../analysis/jrf.RDS")
jrf_list[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "JRF output")
symmetrize Output and ROC
jrf_wadj <- generate_adjacency(jrf_list, ground.truth = adjm)
sjrf_wadj <- symmetrize(jrf_wadj, weight_function = "mean")
plotROC(sjrf_wadj, adjm, plot_title = "ROC curve - JRF Late Integration")
sjrf_wadj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "JRF symmetrize output")
Generate Adjacency and Apply Cutoff
sjrf_adj <- cutoff_adjacency(count_matrices = count_matrices,
weighted_adjm_list = sjrf_wadj,
ground.truth = adjm,
n = 2,
method = "JRF")
## Matrix 1 Mean 95th Percentile Cutoff: 4.940559
## Matrix 2 Mean 95th Percentile Cutoff: 4.907554
## Matrix 3 Mean 95th Percentile Cutoff: 4.856606
sjrf_adj[[1]] %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "JRF adjacency")
Comparison with the Ground Truth
scores.jrf.all <- pscores(adjm, sjrf_adj)
scores.jrf.all$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
plots <- plotg(sjrf_adj)
consesusm <- create_consensus(sjrf_adj, method="vote")
consesusu <- create_consensus(sjrf_adj, method="union")
scores.jrf <- pscores(adjm, list(consesusm))
scoresu.jrf <- pscores(adjm, list(consesusu))
scores.jrf$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
scoresu.jrf$Statistics %>%
datatable(extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('csv', 'excel'),
scrollX = TRUE,
pageLength = 10),
caption = "scores")
source("compare_consensus.R")
ajm_compared <- compare_consensus(consesusm, adjm)
ajm_compared <- compare_consensus(consesusu, adjm)
#est <- PCzinb(as.matrix(count_matrices[[1]]), method="zinb1", maxcard=3, alpha=0.1)
#colnames(est) <- as.character(1:10)
#graph_est <- graph_from_adjacency_matrix(est, mode="undirected")
#plot(graph_est)
Method Comparison
mplot <- list()
for (k in c("TPR", "FPR", "Precision", "F1", "Accuracy")) {
mplot[[k]] <- data.frame(
GENIE3_late = scores.genie3.late$Statistics[[k]],
GENIE3_early = scores.genie3.early$Statistics[[k]],
GRNBoost2_late = scores.grn.late$Statistics[[k]],
GRNBoost2_early = scores.grn.early$Statistics[[k]],
ZILGM_late = scores.zilgm.late$Statistics[[k]],
ZILGM_early = scores.zilgm.early$Statistics[[k]],
JRF = scores.jrf$Statistics[[k]]
)
}
# Convert mplot list into a long data frame for ggplot2
plot_data <- bind_rows(lapply(names(mplot), function(metric) {
data.frame(
Metric = metric,
Method = names(mplot[[metric]]),
Value = as.numeric(mplot[[metric]][1, ])
)
}))
# Extract method groups for coloring and order the Method factor so JRF appears last
plot_data <- plot_data %>%
mutate(Method_Group = case_when(
grepl("GENIE3", Method) ~ "GENIE3",
grepl("GRNBoost2", Method) ~ "GRNBoost2",
grepl("ZILGM", Method) ~ "ZILGM",
grepl("JRF", Method) ~ "JRF"
)) %>%
mutate(Method = factor(Method, levels = c(
"GENIE3_early", "GENIE3_late",
"GRNBoost2_early", "GRNBoost2_late",
"ZILGM_early", "ZILGM_late",
"JRF" # Ensure JRF is last
)))
# Define color palette for each method group
method_colors <- c("GENIE3" = "darkblue", "GRNBoost2" = "darkgreen", "ZILGM" = "orange", "JRF" = "red")
# Create a separate ggplot for each metric, with y-axis limits set to 0-1 and no legend
plots <- lapply(unique(plot_data$Metric), function(metric) {
# Determine if x-axis text should be displayed
show_x_text <- metric %in% c("Accuracy", "F1")
ggplot(plot_data %>% filter(Metric == metric), aes(x = Method, y = Value, fill = Method_Group)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
labs(title = metric, y = "Value", x = NULL) +
scale_y_continuous(limits = c(0, 1)) + # Set y-axis limits
scale_fill_manual(values = method_colors) +
theme_minimal() +
theme(
axis.text.x = if (show_x_text) element_text(angle = 45, hjust = 1) else element_blank(),
axis.title.x = element_blank(),
legend.position = "none" # Remove legend from individual plots
)
})
# Add time data to the plotting dataset
time_data <- data.frame(
Method = names(execution_times),
Time_in_Hours = unlist(execution_times) / 3600
)
time_data$Time_in_Minutes <- time_data$Time_in_Hours * 60
time_data <- time_data[order(time_data$Time_in_Hours), ]
time_data$Method <- factor(time_data$Method, levels = time_data$Method)
# Assign group colors to time_data for consistency
time_data <- time_data %>%
mutate(Method_Group = case_when(
grepl("GENIE3", Method) ~ "GENIE3",
grepl("GRNBoost2", Method) ~ "GRNBoost2",
grepl("ZILGM", Method) ~ "ZILGM",
grepl("JRF", Method) ~ "JRF"
))
# Create the execution time plot
time_plot <- ggplot(time_data, aes(x = Method, y = Time_in_Hours, fill = Method_Group)) +
geom_bar(stat = "identity") +
geom_text(aes(label = sprintf("%.1f min", Time_in_Minutes)), vjust = -0.5) +
labs(title = "Execution Time for Each Method", y = "Time (Hours)", x = NULL) +
scale_fill_manual(values = method_colors) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none"
)
# Add the execution time plot to the existing list of plots
plots <- append(plots, list(time_plot))
# Adjust the layout to accommodate the extra plot
final_plot <- (plots[[1]] | plots[[2]]) /
(plots[[3]] | plots[[4]]) /
(plots[[5]] | plots[[6]]) +
plot_layout(guides = "collect") &
theme(legend.position = "bottom")
print(final_plot)
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=it_IT.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=it_IT.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=it_IT.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=it_IT.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] doRNG_1.8.2 rngtools_1.5.2 patchwork_1.1.1 ZILGM_0.1.1
## [5] RColorBrewer_1.1-3 rsvg_2.6.1 DiagrammeRsvg_0.1 JRF_0.1-4
## [9] pROC_1.18.0 DiagrammeR_1.0.11 gridExtra_2.3 reshape2_1.4.4
## [13] rbenchmark_1.0.0 learn2count_0.3.2 reticulate_1.34.0 DT_0.22
## [17] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
## [21] readr_2.1.2 tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6
## [25] tidyverse_1.3.1 igraph_2.0.3 doParallel_1.0.17 iterators_1.0.14
## [29] foreach_1.5.2 GENIE3_1.16.0
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-3 ellipsis_0.3.2
## [3] XVector_0.34.0 GenomicRanges_1.46.1
## [5] fs_1.5.2 rstudioapi_0.13
## [7] farver_2.1.0 fansi_1.0.3
## [9] lubridate_1.8.0 xml2_1.3.3
## [11] splines_4.1.0 codetools_0.2-18
## [13] bst_0.3-24 pscl_1.5.9
## [15] knitr_1.39 flux_0.3-0.1
## [17] jsonlite_1.8.0 broom_0.8.0
## [19] dbplyr_2.1.1 png_0.1-7
## [21] graph_1.72.0 compiler_4.1.0
## [23] httr_1.4.3 tictoc_1.2.1
## [25] backports_1.4.1 assertthat_0.2.1
## [27] Matrix_1.6-1.1 fastmap_1.1.0
## [29] cli_3.3.0 distributions3_0.2.2
## [31] visNetwork_2.1.2 htmltools_0.5.2
## [33] tools_4.1.0 coda_0.19-4
## [35] gtable_0.3.0 glue_1.6.2
## [37] GenomeInfoDbData_1.2.7 V8_6.0.0
## [39] Rcpp_1.0.8.3 Biobase_2.54.0
## [41] statnet.common_4.10.0 cellranger_1.1.0
## [43] jquerylib_0.1.4 vctrs_0.4.1
## [45] crosstalk_1.2.0 xfun_0.30
## [47] network_1.18.2 rvest_1.0.2
## [49] lifecycle_1.0.1 MASS_7.3-57
## [51] zlibbioc_1.40.0 scales_1.2.0
## [53] hms_1.1.1 MatrixGenerics_1.6.0
## [55] SummarizedExperiment_1.24.0 SingleCellExperiment_1.16.0
## [57] yaml_2.3.5 curl_4.3.2
## [59] sass_0.4.1 rpart_4.1.16
## [61] stringi_1.7.6 highr_0.9
## [63] S4Vectors_0.32.4 caTools_1.18.2
## [65] BiocGenerics_0.40.0 shape_1.4.6
## [67] GenomeInfoDb_1.30.1 rlang_1.1.4
## [69] pkgconfig_2.0.3 matrixStats_0.62.0
## [71] bitops_1.0-7 evaluate_0.15
## [73] lattice_0.20-45 labeling_0.4.2
## [75] htmlwidgets_1.5.4 tidyselect_1.1.2
## [77] gbm_2.2.2 plyr_1.8.7
## [79] magrittr_2.0.3 R6_2.5.1
## [81] IRanges_2.28.0 generics_0.1.2
## [83] DelayedArray_0.20.0 DBI_1.1.2
## [85] pillar_1.7.0 haven_2.5.0
## [87] withr_2.5.0 survival_3.3-1
## [89] RCurl_1.98-1.6 modelr_0.1.8
## [91] crayon_1.5.1 utf8_1.2.2
## [93] iZID_0.0.1 tzdb_0.3.0
## [95] rmarkdown_2.14 grid_4.1.0
## [97] readxl_1.4.0 WeightSVM_1.7-16
## [99] reprex_2.0.1 digest_0.6.29
## [101] numDeriv_2016.8-1.1 mpath_0.4-2.26
## [103] glmnet_4.1-8 stats4_4.1.0
## [105] munsell_0.5.0 bslib_0.3.1